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<?xml version="1.0" encoding="utf-8" ?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN"
"JATS-publishing1.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="1.2" article-type="other">
<front>
<journal-meta>
<journal-id></journal-id>
<journal-title-group>
<journal-title>Journal of Open Source Software</journal-title>
<abbrev-journal-title>JOSS</abbrev-journal-title>
</journal-title-group>
<issn publication-format="electronic">2475-9066</issn>
<publisher>
<publisher-name>Open Journals</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">0</article-id>
<article-id pub-id-type="doi">N/A</article-id>
<title-group>
<article-title>The plebeian Graph Library: A WebGL based network
visualisation and diagnostics package</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-8395-6056</contrib-id>
<name>
<surname>Haldar</surname>
<given-names>Indrajeet</given-names>
</name>
<xref ref-type="aff" rid="aff-1"/>
</contrib>
<aff id="aff-1">
<institution-wrap>
<institution>Graduate School of Design, Harvard University,
USA</institution>
</institution-wrap>
</aff>
</contrib-group>
<pub-date date-type="pub" publication-format="electronic" iso-8601-date="2023-09-08">
<day>8</day>
<month>9</month>
<year>2023</year>
</pub-date>
<volume>¿VOL?</volume>
<issue>¿ISSUE?</issue>
<fpage>¿PAGE?</fpage>
<permissions>
<copyright-statement>Authors of papers retain copyright and release the
work under a Creative Commons Attribution 4.0 International License (CC
BY 4.0)</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>The article authors</copyright-holder>
<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
<license-p>Authors of papers retain copyright and release the work under
a Creative Commons Attribution 4.0 International License (CC BY
4.0)</license-p>
</license>
</permissions>
<kwd-group kwd-group-type="author">
<kwd>JavaScript</kwd>
<kwd>Visualisation</kwd>
<kwd>Graphs</kwd>
<kwd>Networks</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec id="summary">
<title>Summary</title>
<p>Given a large network (greater than one million nodes), visualising
and diagnosing network data has often proven challenging
(<xref alt="Nowogrodzki, 2015" rid="ref-nowogrodzki2015eleven" ref-type="bibr">Nowogrodzki,
2015</xref>). Although there is a wide range of statistical tools to
draw inferences, the esoteric nature of the statistical analysis of
networks limits the communication of the findings to researchers
familiar with these research methods
(<xref alt="Tobi & Kampen, 2018" rid="ref-tobi2018research" ref-type="bibr">Tobi
& Kampen, 2018</xref>). Statistical analyses may not always
capture the nuanced patterns and correlations within complex datasets,
a limitation that visual inspection can overcome
(<xref alt="Vaishnavi et al., 2016" rid="ref-Vaishnavi2016A" ref-type="bibr">Vaishnavi
et al., 2016</xref>). The Plebeian Graph Library (PGL) is a library
that solves for the visualisation of large networks and their
diagnostic study. PGL enables a deeper, more intuitive understanding
of intricate processes such as network diffusion by allowing for
direct, interactive exploration of data bridging the gap between raw
data and actionable insights.</p>
</sec>
<sec id="introduction">
<title>Introduction</title>
<p>PGL is a JavaScript library (written in Typescript
(<xref alt="Bierman et al., 2014" rid="ref-bierman2014understanding" ref-type="bibr">Bierman
et al., 2014</xref>)) designed to facilitate the visualisation and
diagnostic analysis of large-scale network data in browsers using
WebGL, using a backend provided by ThreeJS
(<xref alt="Danchilla, 2012" rid="ref-danchilla2012three" ref-type="bibr">Danchilla,
2012</xref>). Whether dealing with local datasets or data retrieved
from online sources (APIs), PGL provides a versatile platform for
conducting extensive network simulations, physical modelling, and
visualisations whilst offering a range of diagnostic tools for
organising network data using standard search algorithms
(<xref alt="Mattson et al., 2013" rid="ref-mattson2013standards" ref-type="bibr">Mattson
et al., 2013</xref>) such as network diffusions, breadth-first search,
depth-first search and dijkstra’s search algorithm. With a rich set of
diagnostic features, including network condensation, weighted edge
pruning in highly connected graphs, and support for visualisation
techniques like Kamada Kawai layouts
(<xref alt="Kamada & Kawai, 1989" rid="ref-kamada1989algorithm" ref-type="bibr">Kamada
& Kawai, 1989</xref>), hierarchical plots, hive plots and edge
bundling
(<xref alt="Bourqui et al., 2016" rid="ref-bourqui2016multilayer" ref-type="bibr">Bourqui
et al., 2016</xref>), PGL empowers researchers to gain valuable
insights from complex network structures. Additionally, PGL contains
the canonical example of the Zackary’s Karate Club (ZKC) dataset
(<xref alt="Zachary, 1977" rid="ref-zachary1977information" ref-type="bibr">Zachary,
1977</xref>), and Erdosh Reyni Random Graph model
(<xref alt="Li, 2021" rid="ref-li2021brief" ref-type="bibr">Li,
2021</xref>) as a generator to study and compare network
structures.</p>
<p>An illustrative case for the package is to diagnose large-scale
network diffusion. Visualising a clustered network in 3D, where, for
example, the network nodes are displaced vertically according to their
recursive importance, i.e. eigenvector centralities
(<xref alt="Lacobucci et al., 2017" rid="ref-lacobucci2017eigenvector" ref-type="bibr">Lacobucci
et al., 2017</xref>). A diffusion simulation is then run, and insights
and diagnostics of diffusion sequences are gathered. For example, we
can observe graphically whether diffusion first occurs between high
eigenvector centrality nodes across clusters or instead appears in the
groups before spreading to other clusters. This enables the visual
study of the strength of weak ties behaviour
(<xref alt="Granovetter, 1973" rid="ref-granovetter1973strength" ref-type="bibr">Granovetter,
1973</xref>). Exploratory research, analysis, communication, and
documentation of these network behaviours, as mentioned above, would
have been complex using a traditional visualisation library where the
emphasis lies on validation instead of exploratory study and
diagnostics.</p>
</sec>
<sec id="statement-of-need">
<title>Statement of need</title>
<p>PGL addresses several critical needs in large-scale graph data
visualization. Existing software solutions for visualizing large
datasets, such as Gephi
(<xref alt="Bastian et al., 2009" rid="ref-bastian2009gephi" ref-type="bibr">Bastian
et al., 2009</xref>), are limited to local machine installations,
restricting accessibility and compatibility across various devices.
Additionally, browser-based software libraries like Vis.JS
(<xref alt="Almende B.V., 2017" rid="ref-visjs" ref-type="bibr">Almende
B.V., 2017</xref>) and D3
(<xref alt="Bostock et al., 2011" rid="ref-D3" ref-type="bibr">Bostock
et al., 2011</xref>) , which rely on Scalable Vector Graphics (SVG),
often lack the scalability to analyze complex network structures. This
reliance on SVG imposes performance limitations and restricts
visualizations to two dimensions.</p>
<p>In contrast, PGL offers a robust browser-based solution leveraging
WebGL through the ThreeJS Library. This enables it to surpass
traditional two-dimensional representations’ limitations. PGL is
primarily designed for client-side rendering, taking full advantage of
the capabilities of WebGL to deliver dynamic and interactive
visualizations directly within the browser. While it focuses on
client-side rendering, the underlying graph algorithms of PGL can also
be utilized in server-side processes, providing flexibility in
application architecture.</p>
<p>A performance benchmark conducted against D3, an industry-standard
visualization library, showcases PGL’s capabilities. In this test
involving rendering a graph of approximately 5,000 nodes and 200,000
edges, D3-based SVG graphs only achieved a frame rate of 1.5 frames
per second, bottoming at a frame every two seconds with a maximum of
12 frames per second. In contrast, PGL maintained a minimum of 52
frames per second and averaging 58 frames per second under similar
conditions. This benchmark, performed on both Firefox and Chrome
browsers (with negligible differences in performance) on a computer
with an Nvidia RTX 2080 GPU, highlights PGL’s superior performance and
efficiency in rendering complex network visualizations.</p>
<p>Furthermore, PGL’s three-dimensional rendering approach allows for
a more comprehensive range of data stratification methods and
facilitates more immersive and interactive visualizations. The ability
to navigate information-dense networks in three dimensions
significantly reduces visual noise and enhances clarity in diagnosing
large-scale networks. Since its inception, PGL has been instrumental
in my academic research, especially in the exploration of large-scale
social networks. This is documented in my thesis, “On the Mathematics
of Memetics”
(<xref alt="Haldar, 2022" rid="ref-haldar2022mathematics" ref-type="bibr">Haldar,
2022</xref>), where it served as a crucial tool for generating primary
inferences.</p>
</sec>
<sec id="usage">
<title>Usage</title>
<p>Existing network libraries like NetworkX
(<xref alt="Hagberg et al., 2008" rid="ref-hagberg2008exploring" ref-type="bibr">Hagberg
et al., 2008</xref>) strongly influenced the semantics of the graph
library and borrowed some of the semantic ideas from ThreeJS. The
overall structure is to define a Graph Object made of nodes and edges.
Then, modify this graph based on some properties and update the
relevant settings. Lastly, visualise the nodes as point clouds, boxes
or cylinders, and draw out the edges (bundled or not). The following
is a short example of the canonical ZKC dataset visualised in the
library, simulated with Edge bundling.</p>
<p>First, initialize a node project and install the library using
:</p>
<code language="bash">npm i plebeiangraphlibrary</code>
<p>Then</p>
<code language="javascript">// import the library
import * as PGL from "plebeiangraphlibrary";
async function createVisualization() {
// Load up the ZKC dataset
const zkcSimulated = await PGL.SampleData.LoadZKCSimulated();
// Attach the renderer to a div which is on an HTML that the script is linked too
const canvas = document.getElementById("displayCanvas");
// These are some basic options to set up a graph drawing object. Please refer to the documentation for more options
const graphDrawerOptions = {
graph: zkcSimulated,
width: 800,
height: 700,
canvas: canvas,
};
// Initialize a graph with these settings
const graph3d = new PGL.GraphDrawer.GraphDrawer3d(graphDrawerOptions);
await graph3d.init();
// Create the 3d elements for the graph
// first describe a global scaling factor
const bounds = 1;
// Create all the geometry associated with node elements
const nodeVisualElements = PGL.ThreeWrapper.DrawTHREEBoxBasedVertices(
zkcSimulated,
bounds,
0xffffff,
5
);
// add the node geometry to the scene
graph3d.addVisElement(nodeVisualElements);
// then create all the geometry associated with the edge elements
const edgeVisualElements = PGL.ThreeWrapper.DrawTHREEGraphEdgesThick(
zkcSimulated,
bounds,
0xffafcc,
0.02
);
// add the edge geometry to the scene
graph3d.addVisElement(edgeVisualElements);
// by default the camera revolves around the graph, trigger the animation call
function animate() {
requestAnimationFrame(animate);
graph3d.rendercall();
}
animate();
}
createVisualization();</code>
</sec>
<sec id="documentation">
<title>Documentation</title>
<p>Package documentation is available on GitHub. Guides for general
usage and detailed descriptors of all the functions are also included.
Further documentation is available at
[https://www.plebeiangraphlibrary.com/]. Examples are available at
[https://www.plebeiangraphlibrary.com/examples.html]. The example
described above is documented at
[https://github.com/range-et/pgl_example].</p>
</sec>
<sec id="acknowledgements">
<title>Acknowledgements</title>
<p>The Geometry Lab, under the Laboratory for Design Technologies at
the Graduate School of Design at Harvard University, funded this
project.</p>
</sec>
</body>
<back>
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